


'''simple docstring'''


import re
import string

import numpy as np

import datasets


UpperCAmelCase_						:  List[str]				  =						'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

UpperCAmelCase_						:  Union[str, Any]				  =						'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

UpperCAmelCase_						:  Optional[Any]				  =						'\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION   )
class        lowercase__    (		datasets.Metric   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,   citation=_CITATION       ,   inputs_description=_KWARGS_DESCRIPTION       ,   features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""       ,   id="""sequence"""    ),
											            """references""": datasets.Value("""string"""       ,   id="""sequence"""    ),
											        }    )       ,   reference_urls=[]       ,   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case=None       ,   __snake_case=False       ,   __snake_case=False       ,   __snake_case=False       ,   ):
											if regexes_to_ignore is not None:
															for s in regexes_to_ignore:
																			_SCREAMING_SNAKE_CASE					:			str							= np.array([re.sub(__snake_case       ,   """"""       ,   __snake_case    ) for x in predictions]    )
																			_SCREAMING_SNAKE_CASE					:			str							= np.array([re.sub(__snake_case       ,   """"""       ,   __snake_case    ) for x in references]    )
											else:
															_SCREAMING_SNAKE_CASE					:			int							= np.asarray(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= np.asarray(__snake_case    )

											if ignore_case:
															_SCREAMING_SNAKE_CASE					:			str							= np.char.lower(__snake_case    )
															_SCREAMING_SNAKE_CASE					:			List[str]							= np.char.lower(__snake_case    )

											if ignore_punctuation:
															_SCREAMING_SNAKE_CASE					:			int							= string.punctuation.maketrans(""""""       ,   """"""       ,   string.punctuation    )
															_SCREAMING_SNAKE_CASE					:			Tuple							= np.char.translate(__snake_case       ,   table=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			List[str]							= np.char.translate(__snake_case       ,   table=__snake_case    )

											if ignore_numbers:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= string.digits.maketrans(""""""       ,   """"""       ,   string.digits    )
															_SCREAMING_SNAKE_CASE					:			int							= np.char.translate(__snake_case       ,   table=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			List[Any]							= np.char.translate(__snake_case       ,   table=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= predictions == references

											return {"exact_match": np.mean(__snake_case    ) * 100}








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				assert isinstance(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	) and (
				    number >= 0
				), "'number' must been an int and positive"

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or not number % 2:
								# Negatives, 0, 1 and all even numbers are not primes
								return False

				_SCREAMING_SNAKE_CASE					:			List[str]							= range(3				, int(math.sqrt(SCREAMING_SNAKE_CASE__	) + 1	)				, 2	)
				return not any(not number % i for i in odd_numbers	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=1				, **SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= factor * value
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value

				while not is_prime(SCREAMING_SNAKE_CASE__	):
								value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

				if value == first_value_val:
								return next_prime(value + 1				, **SCREAMING_SNAKE_CASE__	)
				return value




